HappyFeat v0.3.0 is available!
You will find the documentation at https://happyfeat.readthedocs.io/.
Note that this version requires to have Python 3.12.8, OpenViBE v3.6.0 or Timeflux installed on your system.
See CHANGELOG.md for more details.
Added
- Support for Timeflux, with a 2-class MI protocol based on PSD (with Welch's method)
- Tutorial for newcomers, using Timeflux
- Dependencies can be installed with conda
- Parameters for the AutoFeat mechanism can be set via menus
- Most visualization tools use plotly, and figures are saved in the workspace
Fixed
- GUI fixes
- Templates updated
- Fixed various crash sources & stability issues
Installation & usage
HappyFeat is available on PyPI as happyfeat:
# install from pypi
python -m pip install happyfeat
# launch
happyfeat Otherwise, you can clone the repository. The entry point of the application is happyfeat/happyfeat_welcome.py:
git clone https://github.com/Inria-NERV/happyFeat.git
conda env create --name <env_name> -f conda_env.yaml
conda activate <env_name>
python -m happyfeat.happyfeat_welcome